The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor reaching an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical structures such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan; it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
One general method of automatic image processing employs feature based recognition techniques to determine the presence of anatomical structures in medical images. However, feature based recognition techniques can suffer from accuracy problems.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible abnormalities for further review. Such possible abnormalities are often called candidates and are considered to be generated by the CAD system based upon the medical images. One type of CAD candidate generator relies upon the cutting plane images provided by MRI and CT scanners. Another is a divergent gradient field response (DGFR) candidate generator (CG) and is considered a good replacement for the cutting planes candidate generator for the detection of possible abnormalities. The DGFR candidate generator is considered an improved technique over the cutting planes candidate generator because it overcomes the main limitation of the cutting planes CG, namely, its 2D limitations. The main idea in DGFR is to detect areas where the (inverted) gradient of the image correlates well with a 3D divergent gradient field, such as the one obtained when considering the (inverted) gradient of 3D Gaussian function. The same principle applies to “convergent” gradient fields and the various concepts can be illustrated using either convergent or divergent fields interchangeably.
This model of a convergent gradient field is well adapted to finding possible anatomical abnormalities such as, for example, cysts, tumors, and polyps from digital medical images. For example, the DGFR candidate generator is well adapted to identifying possible colon polyps from computed tomography (CT) images, since most of the polyps have roughly the shape of a hemisphere attached to the colon wall. The image gradient field for a hemispherical growth is roughly convergent towards the core of the polyp. FIG. 1 illustrates an exemplary hemispherical polyp attached to a colon wall with gradient field vectors pointing toward the core of the polyp. The approach of the DGFR technique is to perform a vector correlation of the image gradient with the gradient of a Gaussian. If the variance σ of the Gaussian is well adapted to the size of the polyp, this correlation will be high at the core or center of the polyp. In order to handle polyps of various sizes, the correlation with the divergent vector field is performed at multiple values of σ, and the responses at the various scales are then combined to produce candidates, usually by taking the location of the maximum response across scales and then applying a threshold to this “combined” response image. FIG. 2 illustrates the correlation of the image gradient of an exemplary polyp with a gradient of a Gaussian of standard deviation σ.
However, such conventional image processing and analysis systems and techniques as described above still often miss potential candidates within a digital medical image. This is due in large part to the fact that such anatomical abnormalities are often not as close to perfectly hemi-spherical as the above DGFR system assumes.
Therefore, a need exists for an improved system and method for detecting and generating candidates from a digital medical image that can better detect asymmetric, flat, and otherwise difficult to detect anatomic structures.